Ai Academy : Deep Learning 4+

Fixed the material of the content, the material are more up to date for 2025 and all information got better and more up to date : 
Now is 80 articles fixed and updated for 2025 to learn ai - deep learning step by step in 80 days : topics covered are :

80 days topics include in this app are :

Intro to ML – ML types, models, train-test split.

ML in iOS – Using ML in apps.

Model Types – Supervised vs. unsupervised.

Regression & Classification – Basics with MNIST.

SGD Math – How it works.

Normal Equation – Prediction without iteration.

Gradient Descent – Concept and application.

Types of GD – Batch, Stochastic, Mini-Batch.

Perceptrons – Deep learning basics.

MLPs – Regression vs. classification.

Activation Functions – ReLU, Sigmoid, etc.

Non-Linearity – Hidden layers & activation.

Intro to Keras – Building models.

Keras APIs – Sequential, Functional, Subclassing.

Keras API Comparison – Sequential vs. Functional.

TensorFlow Tools – TensorBoard, callbacks, saving models.

Hyperparameter Tuning – With Keras Tuner.

Manual vs. Auto Optimization – Tuning models.

Bayesian Optimization – Neural network tuning.

Vanishing Gradient – Explanation.

Weight Initialization – Strategies.

Monetizing AI APIs – Creating paid APIs.

Weight Init: Part 2 – Advanced methods.

Advanced Activation – GELU, Mish, etc.

Batch Norm – How it works.

Batch Norm: Part 2 – Further details.

Batch Norm Parameters – Trainable/non-trainable.

Gradient Clipping – Avoid exploding gradients.

Transfer Learning – Basics.

Transfer Learning Example – Implementing it.

Labeled vs. Unlabeled Data – Differences.

Speeding Up Training – Optimization tricks.

Momentum Optimization – Deep dive.

Momentum vs. Normalization – Comparisons.

Momentum: Part 3 – Advanced details.

NAG Optimizer – Nesterov Accelerated Gradient.

AdaGrad – Origins & proof.

Optimizer Comparison – AdaGrad, RMSProp, Adam.

Adam vs. Other Optimizers – Pros & cons.

Adam & Local Minima – Understanding behavior.

More Optimizers – NAdam, AdaMax, AdamW.

Learning Rate Schedules – Why they matter.

1Cycle Schedule – Explanation.

Gradient Clipping & Weight Init – Combined effect.

LR Scheduling Methods – 1-Cycle, CED, Exponential.

TensorFlow vs. PyTorch vs. MLX – Frameworks compared.

Regularization – Preventing overfitting.

Dropout – Including MC Dropout.

Max-Norm Regularization – Explanation.

Deep vs. Dense Networks – Differences.

Deep Learning Use Cases – Overview.

ML in iOS Apps – Integration.

CNNs – Basics & use cases.

CNN Math – How it works.

RNNs: Part 1 – Sequence modeling.

RNNs: Part 2 – More details.

RNNs & Time Series – Forecasting.

RNN vs. Feedforward – Mathematical comparison.

ARIMA & SARIMA – Before diving into RNNs.

RNN Step-by-Step – Time series forecasting.

Seq2Seq Forecasting – Iterative vs. direct.

LSTMs & Layer Norm – RNN enhancements.

RNNs for NLP – Language modeling.

Why Transformers Win in NLP – Key insights.

Transformers Overview – GPT to DeepSeek.

Transformer Breakthroughs – ChatGPT to DeepSeek.

BERT Explained – Key insights.

How ChatGPT Works – Basics.

ChatGPT vs. BERT – Understanding comparison.

ChatGPT: Step-by-Step – Breakdown.

NLP Mathematics – Behind modern AI models.

Transformers in Vision & Multimodal AI – Expansion.

Autoencoders, GANs, & Diffusion – Overview.

Stacked Autoencoders – Unsupervised pretraining.

Diffusion Models – Breaking them down.

GANs – Deep learning for image generation.

How DALL·E Works – Image synthesis.

Reinforcement Learning – Applications & impact.

DeepNet – Scaling Transformers to 1,000 layers.

DeepSeek-R1 – Advancing LLM reasoning.